This book introduces a new cyberphysical system that combines clinical and basic neuroscience research with advanced data analysis and medical management tools for developing novel applications for the management of epilepsy. The authors describe the algorithms and architectures needed to provide ambulatory, diagnostic and long-term monitoring services, through multi parametric data collection. Readers will see how to achieve in-hospital quality standards, addressing conventional “routine” clinic-based service purposes, at reduced cost, enhanced capability and increased geographical availability. The cyberphysical system described in this book is flexible, can be optimized for each patient and is demonstrated in several case studies.

In the complex landscape of binge eating disorders, a pervasive and intricate challenge unfolds. Binge eating, characterized by Binge eating disorders, is a difficult challenge that requires a nuanced understanding of the underlying neuroscientific mechanisms for effective prevention and intervention strategies. There is a pressing need to bridge the gap between cutting-edge neuroscientific research and the evolving therapeutic landscape. To address this, our groundbreaking book is tailored for academic scholars in the neuroscientific community. We offer a transformative journey into the heart of binge eating disorders, unraveling the mysteries that govern neural circuits, genetic factors, hormonal imbalances, and more. Neuroscientific Insights and Therapeutic Approaches to Eating Disorders is a beacon for researchers, clinicians, and mental health professionals seeking to deepen their comprehension of eating disorders. It addresses the present-day challenges posed by binge eating and presents a roadmap for future research and clinical applications. This comprehensive resource synthesizes the latest findings in neuroscience with innovative therapeutic approaches, ultimately paving the way for improved outcomes. Episodes of excessive food consumption and loss of control demand a nuanced understanding of the underlying neuroscientific mechanisms for effective prevention and intervention strategies. Our present reality is marked by a pressing need to bridge the gap between cutting-edge neuroscientific research and the evolving therapeutic landscape. The intricate relationship between the brain and eating disorders calls for a comprehensive resource that not only dissects the neurobiological foundations but also illuminates the path toward innovative therapeutic approaches.

Advances in technology continue to alter the ways in which we conduct our lives, from the private sphere to how we interact with others in public. As these innovations become more integrated into modern society, their applications become increasingly relevant in various facets of life. Wearable Technologies: Concepts, Methodologies, Tools, and Applications is a comprehensive reference source for the latest scholarly material on the development and implementation of wearables within various environments, emphasizing the valuable resources offered by these advances. Highlighting a range of pertinent topics, such as assistive technologies, data storage, and health and fitness applications, this multi-volume book is ideally designed for researchers, academics, professionals, students, and practitioners interested in the emerging applications of wearable technologies.

Advances in mobile computing have provided numerous innovations that make people’s daily lives easier and more convenient. However, as technology becomes more ubiquitous, corresponding risks increase as well. Managing Security Issues and the Hidden Dangers of Wearable Technologies examines the positive and negative ramifications of emerging wearable devices and their potential threats to individuals, as well as organizations. Highlighting socio-ethical issues, policy implementation, and appropriate usage, this book is a pivotal reference source for professionals, policy makers, academics, managers, and students interested in the security and privacy implications of wearable digital devices.

Technological advancements of recent decades have reshaped the way people socialize, work, learn, and ultimately live. The use of cyber-physical systems (CPS) specifically have helped people lead their lives with greater control and freedom. CPS domains have great societal significance, providing crucial assistance in industries ranging from security to healthcare. At the same time, machine learning (ML) algorithms are known for being substantially efficient, high performing, and have become a real standard due to greater accessibility, and now more than ever, multidisciplinary applications of ML for CPS have become a necessity to help uncover constructive solutions for real-world problems. Real-Time Applications of Machine Learning in Cyber-Physical Systems provides a relevant theoretical framework and the most recent empirical findings on various real-time applications of machine learning in cyber-physical systems. Covering topics like intrusion detection systems, predictive maintenance, and seizure prediction, this book is an essential resource for researchers, machine learning professionals, independent researchers, scholars, scientists, libraries, and academicians.

This proceedings constitutes the post-conference proceedings of the 3rd EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2022, held at Vishnu Institute of Technology, Bhimavaram in Andhra Pradesh, India, in November 26-27, 2022. The theme of IC4S 2022 was: cognitive computing approaches with data mining and machine learning techniques. The 22 full papers were carefully reviewed and selected from 88 submissions. The papers are clustered in thematical issues as follows: machine learning and its applications; cyber security and networking; image processing; IoT applications; smart city eco-system and communications.

This book provides comprehensive coverage of the dependability challenges in today's advanced computing systems. It is an in-depth discussion of all the technological and design-level techniques that may be used to overcome these issues and analyzes various dependability-assessment methods. The impact of individual application scenarios on the definition of challenges and solutions is considered so that the designer can clearly assess the problems and adjust the solution based on the specifications in question. The book is composed of three sections, beginning with an introduction to current dependability challenges arising in complex computing systems implemented with nanoscale technologies, and of the effect of the application scenario. The second section details all the fault-tolerance techniques that are applicable in the manufacture of reliable advanced computing devices. Different levels, from technology-level fault avoidance to the use of error correcting codes and system-level checkpointing are introduced and explained as applicable to the different application scenario requirements. Finally the third section proposes a roadmap of future trends in and perspectives on the dependability and manufacturability of advanced computing systems from the special point of view of industrial stakeholders. Dependable Multicore Architectures at Nanoscale showcases the original ideas and concepts introduced into the field of nanoscale manufacturing and systems reliability over nearly four years of work within COST Action IC1103 MEDIAN, a think-tank with participants from 27 countries. Academic researchers and graduate students working in multi-core computer systems and their manufacture will find this book of interest as will industrial design and manufacturing engineers working in VLSI companies.

Widespread chronic diseases (e.g., heart diseases, diabetes and its complications, stroke, cancer, brain diseases) constitute a significant cause of rising healthcare costs and pose a significant burden on quality-of-life for many individuals. Despite the increased need for smart healthcare sensing systems that monitor / measure patients’ body balance, there is no coherent theory that facilitates the modeling of human physiological processes and the design and optimization of future healthcare cyber-physical systems (HCPS). The HCPS are expected to mine the patient’s physiological state based on available continuous sensing, quantify risk indices corresponding to the onset of abnormality, signal the need for critical medical intervention in real-time by communicating patient’s medical information via a network from individual to hospital, and most importantly control (actuate) vital health signals (e.g., cardiac pacing, insulin level, blood pressure) within personalized homeostasis. To prevent health complications, maintain good health and/or avoid fatal conditions calls for a cross-disciplinary approach to HCPS design where recent statistical-physics inspired discoveries done by collaborations between physicists and physicians are shared and enriched by applied mathematicians, control theorists and bioengineers. This critical and urgent multi-disciplinary approach has to unify the current state of knowledge and address the following fundamental challenges: One fundamental challenge is represented by the need to mine and understand the complexity of the structure and dynamics of the physiological systems in healthy homeostasis and associated with a disease (such as diabetes). Along the same lines, we need rigorous mathematical techniques for identifying the interactions between integrated physiologic systems and understanding their role within the overall networking architecture of healthy dynamics. Another fundamental challenge calls for a deeper understanding of stochastic feedback and variability in biological systems and physiological processes, in particular, and for deciphering their implications not only on how to mathematically characterize homeostasis, but also on defining new control strategies that are accounting for intra- and inter-patient specificity – a truly mathematical approach to personalized medicine. Numerous recent studies have demonstrated that heart rate variability, blood glucose, neural signals and other interdependent physiological processes demonstrate fractal and non-stationary characteristics. Exploiting statistical physics concepts, numerous recent research studies demonstrated that healthy human physiological processes exhibit complex critical phenomena with deep implications for how homeostasis should be defined and how control strategies should be developed when prolonged abnormal deviations are observed. In addition, several efforts have tried to connect these fractal characteristics with new optimal control strategies that implemented in medical devices such as pacemakers and artificial pancreas could improve the efficiency of medical therapies and the quality-of-life of patients but neglecting the overall networking architecture of human physiology. Consequently, rigorously analyzing the complexity and dynamics of physiological processes (e.g., blood glucose and its associated implications and interdependencies with other physiological processes) represents a fundamental step towards providing a quantifiable (mathematical) definition of homeostasis in the context of critical phenomena, understanding the onset of chronic diseases, predicting deviations from healthy homeostasis and developing new more efficient medical therapies that carefully account for the physiological complexity, intra- and inter-patient variability, rather than ignoring it. This Research Topic aims to open a synergetic and timely effort between physicians, physicists, applied mathematicians, signal processing, bioengineering and biomedical experts to organize the state of knowledge in mining the complexity of physiological systems and their implications for constructing more accurate mathematical models and designing QoL-aware control strategies implemented in the new generation of HCPS devices. By bringing together multi-disciplinary researchers seeking to understand the many aspects of human physiology and its complexity, we aim at enabling a paradigm shift in designing future medical devices that translates mathematical characteristics in predictable mathematical models quantifying not only the degree of homeostasis, but also providing fundamentally new control strategies within the personalized medicine era.